Package 'SEERaBomb' reference manual

Title:	SEER and Atomic Bomb Survivor Data Analysis Tools
Description:	Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.
Authors:	Tomas Radivoyevitch [aut, cre], R. Molenaar [ctb]
Maintainer:	Tomas Radivoyevitch <[email protected]>
License:	GPL (>= 2)
Version:	2019.2
Built:	2024-11-02 04:59:12 UTC
Source:	https://github.com/cran/SEERaBomb

SEER and A-Bomb Data Analysis Tools

Description

Creates SEER (Surveillance, Epidemiology and End Results) and Japanese A-bomb survivor data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Depicted below, mkSEER merges cancers and populations of all three of the SEER databases into single cancer and population data frames.

SEER data field positions and names change over the years and the original purpose of SEERaBomb was to buffer/protect R scripts from such changes. A second purpose was to speed up SEER data computations by reducing the data [via pickFields()] to only fields of interest. SEERaBomb now has an additional purpose: estimating relative risks of SEER second cancers after diagnoses of first cancers, using all three SEER databases. Note: SEER no longer includes radiation therapy data by default. Users must thus obtain custom SEER treatment data https://seer.cancer.gov/data/treatment.html.

Details

Package:	SEERaBomb
Type:	Package
Depends:	dplyr, ggplot2, rgl, demography
Suggests:	bbmle
License:	GPL-2
LazyData:	yes
URL:	http://epbi-radivot.cwru.edu/SEERaBomb/SEERaBomb.html

Author(s)

Tom Radivoyevitch ([email protected])

References

Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1973-2015), National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch, released April 2018, based on the November 2017 submission.

Cancer risk vs years Since Diagnosis of other cancer

Description

Computes relative risks (RR) of 2nd cancers over specified intervals of times since diagnoses of a 1st cancer. 2D spline fits are used to produce expected cases E controlling for background risk dedepence on age and calendar year. RR is then O/E where O is the number of observed cases.

Usage

csd(seerSet,brkst=c(0),brksy=c(1975),brksa=c(0),trts=NULL,
                    PYLong=FALSE,firstS="all",exclUnkSurv=FALSE) csd(seerSet,brkst=c(0),brksy=c(1975),brksa=c(0),trts=NULL,
                    PYLong=FALSE,firstS="all",exclUnkSurv=FALSE)

Arguments

`seerSet`	A seerSet object produced by mk2D().
`brkst`	Vector of breaks in years used to form Time intervals/bins since diagnosis. An upper limit of 100, well beyond 40 years of SEER follow up currently available, is assumed/added to brkst internally, and should thus not be in brkst.
`brksy`	Vector of breaks used to form groups of calendar Year at 1st cancer diagnosis intervals/bins. An upper limit of yearEnd (last year in SEER; a seerSet field) is assumed/added to brksy internally.
`brksa`	Vector of breaks used to form groups of Age at 1st cancer diagnosis intervals/bins. An upper limit of 126 is assumed.
`trts`	Character vector of treatments of interest. Default of NULL => all levels in seerSet's canc$trt.
`PYLong`	Set true if in addition to O and E for each tsd interval you also want PY strips for each individual; having these big dataframes slows saving seerSets, so only fetch if needed.
`firstS`	Character vector of first cancers of interest. Default of "all" sets it to the vector of all cancers in the seerSet field cancerS, which is created when the object is first created by seerSet().
`exclUnkSurv`	Set true if you wish to exclude all cases with unknown survival times as a marker of bad data.

Value

The input with an L component added to it or extended it if it already existed. Each component of L is a nested list of lists that can yield second cancer relative risks as a function of time since 1st cancer diagnosis. The most recent component of L is also provided as a data.frame seerSet$DF produced internally using getDF.

Note

This function was developed with support from the Cleveland Clinic Foundation.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
pm=simSeerSet()
pm=mk2D(pm)
pm$canc
pm=csd(pm,brkst=c(0,5),brksy=c(1975,2000),brksa=c(0,50),trts=c("noRad","rad"))
pm
library(ggplot2)
theme_set(theme_gray(base_size = 16)) 
theme_update(legend.position = "top")
g=qplot(x=t,y=RR,data=subset(pm$DF,cancer1=="A"&cancer2=="B"),col=trt,geom=c("line","point"),
        xlab="Years Since First Cancer Diagnosis",ylab="Relative Risk")
g=g+facet_grid(yearG~ageG,scales="free")+geom_abline(intercept=1, slope=0) 
g+geom_errorbar(aes(ymin=rrL,ymax=rrU,width=.15))

## End(Not run)
## Not run: 
library(SEERaBomb)
pm=simSeerSet()
pm=mk2D(pm)
pm$canc
pm=csd(pm,brkst=c(0,5),brksy=c(1975,2000),brksa=c(0,50),trts=c("noRad","rad"))
pm
library(ggplot2)
theme_set(theme_gray(base_size = 16)) 
theme_update(legend.position = "top")
g=qplot(x=t,y=RR,data=subset(pm$DF,cancer1=="A"&cancer2=="B"),col=trt,geom=c("line","point"),
        xlab="Years Since First Cancer Diagnosis",ylab="Relative Risk")
g=g+facet_grid(yearG~ageG,scales="free")+geom_abline(intercept=1, slope=0) 
g+geom_errorbar(aes(ymin=rrL,ymax=rrU,width=.15))

## End(Not run)

Event vs years Since Diagnosis

Description

Computes relative risks (RR) of second cancers over specified years-since-diagnosis intervals. SEER incidence rates are used to compute background/expected numbers of cases E, sex, age, and calendar year specifically. RR = O/E where O and E are the numbers of observed and expected cases.

Usage

esd(d,srfF,srfM,brkst=c(0,2,5),brksy=NULL) esd(d,srfF,srfM,brkst=c(0,2,5),brksy=NULL)

Arguments

`d`	Input data.frame with columns: yrdx, agedx, sex, py at risk (in years), cancer1, and cancer2. Cancer1 and cancer2 should use standard SEERaBomb cancer names, see mapCancs. Cases not ending in a second cancer should have cancer2 set to "none".
`srfF`	Female incidence surface. Output D of mk2D for females, for cancers in cancer2
`srfM`	Male incidence surface. Output D of mk2D for males, for cancers in cancer2
`brkst`	Vector of breaks in years used to form times since diagnosis intervals/bins.
`brksy`	Vector of breaks of calendar years to show trends. Leave NULL for all in one.

Value

data.frame with observed and expected cases, RR, and RR CI for each time since diagnosis interval.

Note

This function was developed with support from the Cleveland Clinic Foundation.

Author(s)

Tom Radivoyevitch ([email protected])

Converts a seerSet$L series to a data.frame

Description

Creates a data.frame of observed and expected cases for each first and second cancer and treatment. csd() calls this internally for the most recent time series, so it may not need to be called directly.

Usage

getDF(seerSet,srs=NULL)getDF(seerSet,srs=NULL)

Arguments

`seerSet`	seerSet object produced by csd().
`srs`	Series. The time series of interest. NULL (default) implies the currently active series, which is the most recent. A number i implies the ith series. A string identifies the series by name (numeric vectors will be coerced to such a string via paste0("b",paste(brks,collapse="_")) where brks = vector of time breakpoints.

Value

A data.frame in long format that can be used by ggplot.

Note

I envision getting away from saving multiseries seerSet objects and instead just saving several DF outputs of getDF. Besides smaller objects, a reason for this is that two L objects out of csd can now be confounded if they have the same time since diagnosis series but a different series for age and/or year of diagnosis.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run:  
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
canc=canc%>%select(-reg,-recno,-agerec,-numprims,-COD,
          -age19,-age86,-radiatn,-ICD9,-db,-histo3) 
popsa=popsae%>%group_by(db,race,sex,age,year)%>%summarize(py=sum(py)) # sum on regs
pm=seerSet(canc,popsa,Sex="male",ageStart=0,ageEnd=100) #pooled (races) male seerSet
pm=mk2D(pm,secondS=c("AML","MDS"))
firstS=c("NHL","MM")
pm=csd(pm,brkst=c(0,1,5),trts=c("rad","noRad"),firstS=firstS)
pm$DF 
getDF(pm)

## End(Not run)
## Not run:  
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
canc=canc%>%select(-reg,-recno,-agerec,-numprims,-COD,
          -age19,-age86,-radiatn,-ICD9,-db,-histo3) 
popsa=popsae%>%group_by(db,race,sex,age,year)%>%summarize(py=sum(py)) # sum on regs
pm=seerSet(canc,popsa,Sex="male",ageStart=0,ageEnd=100) #pooled (races) male seerSet
pm=mk2D(pm,secondS=c("AML","MDS"))
firstS=c("NHL","MM")
pm=csd(pm,brkst=c(0,1,5),trts=c("rad","noRad"),firstS=firstS)
pm$DF 
getDF(pm)

## End(Not run)

Get fields from SEER SAS file

Description

Converts the SAS file in the SEER ‘incidence’ directory into a data frame in R.

Usage

getFields(seerHome="~/data/SEER")getFields(seerHome="~/data/SEER")

Arguments

seerHome

The directory that contains the SEER ‘population’ and ‘incidence’ directories.

Details

SEER provides a SAS file for reading SEER ASCII data files into SAS. This file is parsed by getFields() to generate a data frame in R that contains all of the SEER fields. This data frame describes these fields in terms of their names (short and long forms), their starting points, and their widths.

Value

A data frame with one row for each field and columns that contain corresponding starting positions, widths, sas names, short names, and expansions thereof.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
(df=getFields())
head(df,20)

## End(Not run)
## Not run: 
library(SEERaBomb)
(df=getFields())
head(df,20)

## End(Not run)

Computes A-bomb incidences

Description

Creates A-bomb survivor incidence rates and confidence intervals.

Usage

incidAbomb(d)incidAbomb(d)

Arguments

`d`	Tibble, typically grouped, with DG column ending 1st block and py starting the last.

Details

The columns DG and py must exist in d, in that order. Person-year weighted means will be formed of any columns between them. Its OK if none exist. Sums are formed on py and anything to their right. It is assumed that cancer types begin after py, with at most upy and/or subjects intervening them.

Value

A tibble data frame, summarized by groups, with cancers after py in a new cancers column, and new columns O (observed cases), incid and incid 95% CI limits LL and UL.

Author(s)

Tom Radivoyevitch ([email protected])

Computes SEER incidences

Description

Creates SEER incidence rates and confidence intervals.

Usage

incidSEER(canc,popsae,cancers)incidSEER(canc,popsae,cancers)

Arguments

`canc`	data frame of cancer cases
`popsae`	data frame of person years at risk
`cancers`	character vector of cancer types

Details

This left joins popsae and cancers in canc.

Value

A data frame with observed cases (O), incid, and incid 95% CI limits LL and UL.

Author(s)

Tom Radivoyevitch ([email protected])

Map ICD9 and ICD-O3 codes to cancers

Description

Adds a factor cancer with easily recognizable levels to a SEER cancer data.frame.

Usage

mapCancs(D)mapCancs(D)

Arguments

`D`	A data frame that includes ICD9 and histo3 as columns.

Value

The input data frame with an additional cancer column added on.

Note

This is used by mkSEER() when it generates R binaries of the SEER data. Otherwise it provides current cancer definitions (seen by looking at the function definition).

Author(s)

Tom Radivoyevitch ([email protected])

Examples

library(SEERaBomb)
mapCancs # shows default definitions
library(SEERaBomb)
mapCancs # shows default definitions

Map CODs to strings

Description

Maps integer cause of death (COD) codes in COD of a SEER cancer data frame to a factor CODS with recognizable levels. This is a bit slow, so it is called within mkSEER.

Usage

mapCODs(D)mapCODs(D)

Arguments

`D`	A data frame that includes COD as a column.

Value

The input data frame with an additional CODS column added on.

Note

Typing mapCODs dumps the function definition and thus the mapping used.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

library(SEERaBomb)
mapCODs # shows default definitions
library(SEERaBomb)
mapCODs # shows default definitions

Map registry codes to acronyms

Description

Maps codes for SEER registries to 2-letter acronyms and corresponding descriptions.

Usage

mapRegs(code=NA)mapRegs(code=NA)

Arguments

code

Full SEER codes as found in SEER Cancer files. Add 1500 to popuation file codes get such cancer file codes. If this argument is missing (the default) a full dataframe of symbols and descriptions is returned.

Value

A dataframe of SEER registry symbols and descriptions with rownames such as "1501" for sf (san francisco) and "1520" for dM (detroit Michigan), or just the symbol if the rowname is given. Note that city characters are in lower case and state characters are in upper case.

Note

This function is used by mkSEER when it generates merged R binaries. It is exposed to provide quick access to registry acronym definitions.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

library(SEERaBomb)
mapRegs(1501)
mapRegs()
library(SEERaBomb)
mapRegs(1501)
mapRegs()

Map treatment codes to factor

Description

Uses SEER codes in the SEER field radiatn to add a factor named trt with levels "noRad","rad", and "unk" to a cancer data frame.

Usage

mapTrts(D)mapTrts(D)

Arguments

`D`	A SEER cancer data frame that includes the field radiatn as a column.

Value

The input data frame with an additional trt column added to its end.

Note

This function is used by mkSEER when it generates merged R binaries. It is exposed to state the default definition of trt and, by way of example, to show how to override it.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

library(SEERaBomb)
mapTrts # exposes default definition of trt
library(SEERaBomb)
mapTrts # exposes default definition of trt

Make 2D-spline fits of incidences

Description

Produces two dimensional (2D) spline fits of cancer incidence versus age and calendar year, with interactions. In conjunction with person years (PY) at risk, this is used in csd() to produce expected numbers of cases under a null hypothesis that prior cancers do not impact subsequent cancer risks.

Usage

mk2D(seerSet, knots=5, write=FALSE, outDir="~/Results",txt=NULL,secondS=NULL)mk2D(seerSet, knots=5, write=FALSE, outDir="~/Results",txt=NULL,secondS=NULL)

Arguments

`seerSet`	Object of class seerSet, i.e. output list of seerSet().
`knots`	Base number of knots; overrides are in place for some cancers.
`write`	TRUE = write 2D fits to files. The fits can be >300 MB and take >60 seconds to write, so leave FALSE unless you need it.
`outDir`	Folder that will hold the output files.
`txt`	Additional text to distinguish files with different cancer lists. This may be useful during spline fit development.
`secondS`	Charcter vector of second cancers of interest (note: I often capitalize the final S of vectors of Strings).

Value

The input seerSet with an additional data frame D added to this list. D holds background/expected incidences over a 1-year resolution age-year grid.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
(pm=simSeerSet())
(pm=mk2D(pm))
names(pm)
head(pm$D)
tail(pm$D)

## End(Not run)
## Not run: 
library(SEERaBomb)
(pm=simSeerSet())
(pm=mk2D(pm))
names(pm)
head(pm$D)
tail(pm$D)

## End(Not run)

Make Abomb Binaries

Description

Converts Abomb files ‘lsshempy.csv’ and ‘lssinc07.csv’ into tibbles heme and solid in the file ‘abomb.RData’.

Usage

mkAbomb(AbombHome="~/data/abomb")mkAbomb(AbombHome="~/data/abomb")

Arguments

AbombHome

Directory with Abomb files. Should be writable by user.

Details

The files ‘lsshempy.csv’ and ‘lssinc07.csv’ can be found under The incidence of leukemia, lymphoma and multiple myeloma among atomic bomb survivors: 1950-2001 and Life Span Study Cancer Incidence Data, 1958-1998 of the Radiation Effects Research Foundation (RERF) website http://www.rerf.or.jp/.

Value

None. This function is called for its side-effect of producing ‘abomb.RData’.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
mkAbomb()
load("~/data/abomb/abomb.RData")
View(heme)

## End(Not run)
## Not run: 
library(SEERaBomb)
mkAbomb()
load("~/data/abomb/abomb.RData")
View(heme)

## End(Not run)

Make Demographics Tables

Description

Provides, in an Excel file, quartiles of age at diagnoses in one sheet and median overall survival times on a second. Many tables are placed in each sheet. One Excel file is produced per cancer type.

Usage

mkDemographics(canc,outDir="~/Results/SEERaBomb")mkDemographics(canc,outDir="~/Results/SEERaBomb")

Arguments

`canc`	A dataframe that includes cancer, age at diagnosis (agedx), age (grouped agedx), race, sex, year (grouped), COD, surv, and trt.
`outDir`	Folder of the Excel file(s) that will be generated.

Value

Returned invisibly is a list of data frames corresponding to tables of the Excel file(s).

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
rm(list=ls())
load("~/data/SEER/mrgd/cancDef.RData")
canc$year=cut(canc$yrdx,c(1973,2003,2009,2015),include.lowest = T,dig.lab=4)
canc$age=cut(canc$agedx,c(0,40,50,60,70,80,90,126),include.lowest = T)
canc=canc%>%filter(surv<9999)
canc=canc%>%select(-age86,-radiatn,-chemo,-db,-casenum,-modx,-seqnum,-yrbrth,-ICD9,-reg,-histo3)
canc=canc%>%filter(cancer%in%c("AML","MDS","MPN"))
head(canc,3)
mkDemographics(canc)

## End(Not run)
## Not run: 
library(SEERaBomb)
rm(list=ls())
load("~/data/SEER/mrgd/cancDef.RData")
canc$year=cut(canc$yrdx,c(1973,2003,2009,2015),include.lowest = T,dig.lab=4)
canc$age=cut(canc$agedx,c(0,40,50,60,70,80,90,126),include.lowest = T)
canc=canc%>%filter(surv<9999)
canc=canc%>%select(-age86,-radiatn,-chemo,-db,-casenum,-modx,-seqnum,-yrbrth,-ICD9,-reg,-histo3)
canc=canc%>%filter(cancer%in%c("AML","MDS","MPN"))
head(canc,3)
mkDemographics(canc)

## End(Not run)

Make RR Excel file from csd output

Description

Provides relative risks (RR) organized by 1st and 2nd cancers, times since 1st cancer diagnoses, and 1st cancer treatment. RR = O/E where O = observed cases and E = cases expected under a null hypothesis that prior cancers do not impact subsequent risks. If flip = FALSE (default), sheets = 1st cancers and rows = 2nd cancers, else sheets = 2nd cancers and rows = 1st cancers; columns are always intervals of years since diagnosis, in 1st cancer treatment blocks. RR CI and observed numbers are included in each data cell.

Usage

mkExcelCsd(seerSet,tsdn,biny="[1975,2017)",bina="(0,126]",
                        outDir="~/Results",outName=NULL,flip=FALSE)mkExcelCsd(seerSet,tsdn,biny="[1975,2017)",bina="(0,126]",
                        outDir="~/Results",outName=NULL,flip=FALSE)

Arguments

`seerSet`	A seerSet list after it has been processed by csd().
`tsdn`	Name of set of times since diagnosis. This is based on the brkst argument to csd(). If length >1 a brkst vector is assumed and coerced/collapsed to a tsdn string.
`biny`	Year at DX interval.
`bina`	Age at DX interval.
`outDir`	Folder of the Excel file that will be generated.
`outName`	if null (default), Excel file name = seerSet base file name (bfn) + tsdn, else it is outName. Eitherway, "Flipped" is appended to the name if flip is TRUE.
`flip`	If FALSE, sheets are first cancers, rows seconds. If TRUE, sheets are second cancers, rows firsts.

Value

Returned invisibly, a list of data frames corresponding to sheets of the Excel file.

Note

Outputs are for a given sex. Races are typically pooled.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
pm=simSeerSet()
pm=mk2D(pm) 
mybrks=c(0,1,5,10)
pm=csd(pm,brkst=mybrks,trts=c("noRad","rad")) 
(lab=paste0("b",paste(mybrks,collapse="_")))
(L=mkExcelCsd(pm,lab))
(L=mkExcelCsd(pm,lab,flip=TRUE))

## End(Not run)

## Not run: 
library(SEERaBomb)
pm=simSeerSet()
pm=mk2D(pm) 
mybrks=c(0,1,5,10)
pm=csd(pm,brkst=mybrks,trts=c("noRad","rad")) 
(lab=paste0("b",paste(mybrks,collapse="_")))
(L=mkExcelCsd(pm,lab))
(L=mkExcelCsd(pm,lab,flip=TRUE))

## End(Not run)

Make Life Tables

Description

Makes life tables from mortality data binaries produced by mkMrt() and places the files in the same folder.

Usage

 mkLT(mrtHome="~/data/usMort",input="mrt.RData",output="ltb.RData")mkLT(mrtHome="~/data/usMort",input="mrt.RData",output="ltb.RData")

Arguments

`mrtHome`	Directory that contains the mortality data binary.
`input`	File that contains the mortality data binary.
`output`	File that will contain the life tables.

Value

None. This function is called for its side-effect of producing male and female life tables in files.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
mkLT()
load("~/data/usMort/ltb.RData")
tail(ltb$Female)

## End(Not run)
## Not run: 
library(SEERaBomb)
mkLT()
load("~/data/usMort/ltb.RData")
tail(ltb$Female)

## End(Not run)

Make mortality binaries

Description

Gets mortality data from the Human Mortality Database http://www.mortality.org/ and puts it in the file ‘mrt.RData’.

Usage

 mkMrt(username,passwd,country="USA",mrtHome="~/data/usMort")mkMrt(username,passwd,country="USA",mrtHome="~/data/usMort")

Arguments

`username`	Username of Human Mortality Database account.
`passwd`	Password of Human Mortality Database account.
`country`	This should probably stay at its default of USA.
`mrtHome`	Directory that will contain the mortality data binary. Should be writable by user.

Value

None. This function is called for its side-effect of producing ‘mrt.RData’.

Author(s)

Tom Radivoyevitch ([email protected])

References

Barbieri M, Wilmoth JR, Shkolnikov VM, et al. Data Resource Profile: The Human Mortality Database (HMD). Int J Epidemiol. 2015;44: 1549-1556.

Examples

## Not run: 
library(SEERaBomb)
mkMrt("username", "password")# sub in your personal account info
load("~/data/usMort/mrt.RData")
head(mrt$Female)

## End(Not run)
## Not run: 
library(SEERaBomb)
mkMrt("username", "password")# sub in your personal account info
load("~/data/usMort/mrt.RData")
head(mrt$Female)

## End(Not run)

Make mortality binaries from local HMD data files

Description

Converts locally installed Human Mortality Data http://www.mortality.org/ into an R binary file ‘mrtCOUNTRY.RData’.

Usage

mkMrtLocal(country="USA",mrtHome="~/data/mrt",
                    mrtSrc1="~/data/hmd_countries",
                    mrtSrc2="~/data/hmd_statistics/death_rates/Mx_1x1"
                    )mkMrtLocal(country="USA",mrtHome="~/data/mrt",
                    mrtSrc1="~/data/hmd_countries",
                    mrtSrc2="~/data/hmd_statistics/death_rates/Mx_1x1"
                    )

Arguments

`country`	Default is USA. See names of subfolders of ‘hmd_countries’ for other options.
`mrtHome`	Directory that will contain the mortality data binary. Should be writable by user.
`mrtSrc1`	Directory with hmd_countries data (first choice of files = "all HMD countries").
`mrtSrc2`	Directory with hmd_statistics data (second choice of files = "all HMD statistics").

Value

None. This function is called for its side-effect of producing ‘mrt.RData’ from HMD files organized as all HMD countries or all HMD statistics on the HMD download page (you need at least one of these).

Author(s)

Tom Radivoyevitch ([email protected])

References

Barbieri M, Wilmoth JR, Shkolnikov VM, et al. Data Resource Profile: The Human Mortality Database (HMD). Int J Epidemiol. 2015;44: 1549-1556.

Examples

## Not run: 
library(SEERaBomb)
mkMrtLocal() 
load("~/data/mrt/mrtUSA.RData")
head(mrt$Female)

## End(Not run)
## Not run: 
library(SEERaBomb)
mkMrtLocal() 
load("~/data/mrt/mrtUSA.RData")
head(mrt$Female)

## End(Not run)

Make R binaries of SEER data.

Description

Converts SEER ASCII text files into large R binaries that include all cancer types and registries combined.

Usage

mkSEER(df,seerHome="~/data/SEER",outDir="mrgd",outFile="cancDef",
                  indices = list(c("sex","race"), c("histo3","seqnum"),  "ICD9"),
                  writePops=TRUE,writeRData=TRUE,writeDB=FALSE)mkSEER(df,seerHome="~/data/SEER",outDir="mrgd",outFile="cancDef",
                  indices = list(c("sex","race"), c("histo3","seqnum"),  "ICD9"),
                  writePops=TRUE,writeRData=TRUE,writeDB=FALSE)

Arguments

`df`	A data frame that was the output of `pickFields()`. This determines which fields to transfer. Using the output of `getFields()` is a common mistake that must be avoided.
`seerHome`	The directory that contains the SEER ‘population’ and ‘incidence’ directories. This should be writable by the user.
`outDir`	seerHome subdirectory to write to. Default is ‘mrgd’ for all registries merged together.
`outFile`	Base name of the SQLite database and cancer binary. Default = cancDef (Cancer Default).
`indices`	Passed to `copy_to()` in dplyr.
`writePops`	TRUE if you wish to write out the population data frame binaries. Doing so takes ~10 seconds, so savings of FALSE are small.
`writeRData`	TRUE if you wish to write out the cancer data frame binary. Writing files takes most of the time.
`writeDB`	TRUE if you wish to write cancer, popga, popsa, and popsae data frames to SQLite database tables.

Details

This function uses the R package LaF to access the fixed-width format data files of SEER. LaF is fast, but it requires knowledge of all the widths of columns wanted, as well as the the widths of unwanted stretches in between. This knowledge is produced by getFields() and pickFields() combined. It is passed to mkSEER() via the argument df.

Value

None, it produces R binary files of the SEER data.

Note

This takes a substantial amount of RAM (it works on a Mac with 16 GB of RAM) and time (~3 minutes using default fields).

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
(df=getFields())
(df=pickFields(df))
# the following will take a several minutes, but may only need 
# to be done roughly once per year, with each release.
mkSEER(df)

## End(Not run)
## Not run: 
library(SEERaBomb)
(df=getFields())
(df=pickFields(df))
# the following will take a several minutes, but may only need 
# to be done roughly once per year, with each release.
mkSEER(df)

## End(Not run)

Mortality vs years Since Diagnosis

Description

Computes relative risks (RR) of death over specified years-since-diagnosis intervals. US mortality rates obtained via the R package demography are used to compute background death dedepence on age and calendar year. RR is then O/E where O and E are the number of observed and expected cases.

Usage

msd(canc,mrt,brkst=c(0,2,5),brksy=NULL) msd(canc,mrt,brkst=c(0,2,5),brksy=NULL)

Arguments

`canc`	Input data.frame with columns: yrdx, agedx, sex, surv (in years), and status (1=dead).
`mrt`	List with male and female fields, each matrices with mortality rates vs year and age.
`brkst`	Vector of breaks in years used to form Times since diagnosis intervals/bins.
`brksy`	Vector of breaks of calendar Years to show mortality trends. Leave NULL for all in one.

Value

data.frame with observed and expected cases, RR, and RR CI for each tsd interval.

Note

This function was developed with support from the Cleveland Clinic Foundation.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #loads in canc
lu=canc%>%filter(cancer=="lung")
lu=lu%>%mutate(status=as.numeric(COD>0))%>%select(yrdx,agedx,sex,surv,status) 
lu=lu%>%mutate(surv=round((surv+0.5)/12,3))#convert surv to years

# library(demography)
# d=hmd.mx("USA", "username", "password") #make an account and put your info in here
# mrt=d$rate
# save(mrt,file="~/data/usMort/mrt.RData")
load("~/data/usMort/mrt.RData"); object.size(mrt)# 250kb
brks=c(0,0.5,3,6,10,15,20,25)    
(dlu=msd(lu,mrt,brkst=brks))

## End(Not run)
## Not run: 
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #loads in canc
lu=canc%>%filter(cancer=="lung")
lu=lu%>%mutate(status=as.numeric(COD>0))%>%select(yrdx,agedx,sex,surv,status) 
lu=lu%>%mutate(surv=round((surv+0.5)/12,3))#convert surv to years

# library(demography)
# d=hmd.mx("USA", "username", "password") #make an account and put your info in here
# mrt=d$rate
# save(mrt,file="~/data/usMort/mrt.RData")
load("~/data/usMort/mrt.RData"); object.size(mrt)# 250kb
brks=c(0,0.5,3,6,10,15,20,25)    
(dlu=msd(lu,mrt,brkst=brks))

## End(Not run)

National Vital Statistic Report (nvsr) Data

Description

US mortality rates (probability of death that year) in 2010; report published Nov. 2014.

Usage

nvsr
nvsr

Format

A data frame with the following columns.

age: Single-year resolution ages up to 99.5.
p: Pooled sexes and races.
pm: Pooled races, males.
pf: Pooled races, females.
w: Whites, sexes pooled.
b: Blacks, sexes pooled.
o: Others, sexes pooled.
wm: White males.
wf: White females.
bm: Black males.
bf: Black females.
om: Other males.
of: Other females.

References

National Vital Statistics Reports, Vol. 63, No. 7, November 6, 2014 http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_07.pdf

Examples

library(SEERaBomb)
head(nvsr)
library(SEERaBomb)
head(nvsr)

National Vital Statistic Report (nvsr01) Data

Description

US mortality rates (probability of death that year) in 2001 (Report 52_14).

Usage

nvsr01
nvsr01

Format

A data frame with the following columns.

age: Single-year resolution ages up to 99.5.
p: Pooled sexes and races.
pm: Pooled races, males.
pf: Pooled races, females.
wm: White males.
wf: White females.
bm: Black males.
bf: Black females.

Details

This data is used to extrapolate PY at risk in SEER population files from 85+ to older ages.

References

ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/NVSR/52_14/

Examples

library(SEERaBomb)
head(nvsr01)
library(SEERaBomb)
head(nvsr01)

Primary to Secondary

Description

Using a SEER data frame, this function computes times between primary and secondary cancers. In the resulting data frame, surv and status can be analyzed at the individual level, e.g. using Cox regression.

Usage

p2s(canc,firstS,secondS,yrcut=2010) p2s(canc,firstS,secondS,yrcut=2010)

Arguments

`canc`	Data frame produced by mkSEER().
`firstS`	Vector of names (as Strings) of first cancers you wish to consider.
`secondS`	Vector of names of second cancers you wish to consider.
`yrcut`	Only cases diagnosed in yrcut or newer are analyzed. The default of 2010 is the year AML cases after MDS began to be entered into SEER as second cancers; before they were considered to be part of the first cancer. This function facilitates studies of the rate at which myeloid neoplasms such as MDS progress to AML.

Value

Data frame with a row for each primary (first cancer) diagnosed on or after yrcut. The surv column holds the time in months to last follow up or death (status=0), or to the time of diagnosis of the second cancer (status=1).

Author(s)

Remco J. Molenaar ([email protected] )

Examples

## Not run: 
# 

## End(Not run)
## Not run: 
# 

## End(Not run)

Pick SEER fields of interest

Description

Reduces the full set of SEER data fields to a smaller set of interest. SEER fields are rows of the input and output dataframes of this function. The output dataframe differs from the input dataframe not only in there being fewer rows but also in there being an additional column needed by mkSEER() downstream.

Usage

pickFields(sas,picks=c("casenum","reg","race","sex","agedx",
        "yrbrth","seqnum","modx","yrdx","histo3",
        "ICD9","COD","surv","radiatn","chemo"))pickFields(sas,picks=c("casenum","reg","race","sex","agedx",
        "yrbrth","seqnum","modx","yrdx","histo3",
        "ICD9","COD","surv","radiatn","chemo"))

Arguments

`sas`	A data frame created by `getFields()` using the SAS file found in the ‘incidence’ directory of `seerHome`, the root of the SEER ASCII data installation.
`picks`	Vector of names of variables of interest. This set should not be smaller than the default.

Details

R binaries become too large if all of the fields are selected. SEERaBomb is faster than SEER*Stat because it tailors/streamlines the database to your interests. The default picks are a reasonable place to start; if you determine later that you need more fields, you can always rebuild the binaries. Grabbing all fields is discouraged, but if you want this anyway, note that you still need pickFields() to create a data type column, i.e. you cannot bypass pickFields() by sending the output of getFields() straight to mkSEER().

Value

The SAS-based input data frame sas, shortened to just the rows of picks, and expanded to include spacer rows of fields of no interest pooled into single strings: the width of such a spacer row is equal to the distance in bytes between the fields of interest above and below it. This data frame is then used by laf_open_fwf() of LaF in mkSEER() to read the SEER files. Proper use of this function, and of the SEER data in general, requires an understanding of the contents of ‘seerdic.pdf’ in the ‘incidence’ directory of seerHome.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
(df=getFields())
(df=pickFields(df))


## End(Not run)
## Not run: 
library(SEERaBomb)
(df=getFields())
(df=pickFields(df))


## End(Not run)

Plot 2D cancer incidence splines

Description

Plots splines of incidence versus age and calendar year produced by mk2D.

Usage

plot2D(seerSet, write=TRUE,outDir="~/Results/plots",col="red")plot2D(seerSet, write=TRUE,outDir="~/Results/plots",col="red")

Arguments

`seerSet`	seerSet object after it is processed by mk2D.
`write`	TRUE if you want to write images to a seerSet subfolder. The name of this subfolder is the basefilename (bfn) of the seerSet.
`outDir`	Parent folder of seerSet subfolders.
`col`	Color of surface plot.

Details

A plot will be produced for each cancer fitted by mk2D. For the first of these, RGL will open a new X11 window. Adjustments of size and angle of this first plot will hold for all subsequent plots. After each plot, the user hits any key to write the plot to a file and advance through the list of cancers.

Value

None, results go to the screen and to png files.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
n=simSeerSet()
n=mk2D(n) 
plot2D(n)

## End(Not run)
## Not run: 
library(SEERaBomb)
n=simSeerSet()
n=mk2D(n) 
plot2D(n)

## End(Not run)

Second cancer risk vs attained age after first cancer

Description

Computes absolute risk of 2nd cancers as a function of attained age after first cancer.

Usage

riskVsAge(canc,firstS=c("NHL","HL","MM"),
                secondS=c("AML","MDS"),brksa=c(0,30,50,60,70,80)) riskVsAge(canc,firstS=c("NHL","HL","MM"),
                secondS=c("AML","MDS"),brksa=c(0,30,50,60,70,80))

Arguments

`canc`	canc made by mkSEER().
`firstS`	Character vector of first cancers of interest.
`secondS`	Character vector of second cancers of interest.
`brksa`	Vector of breaks in years used to form attained age intervals.

Value

data.frame with incidence vs age.

Note

This function was developed with support from the Cleveland Clinic Foundation.

Author(s)

Tom Radivoyevitch ([email protected])

Join SEER cancers and PY

Description

Creates a sex-specific list of cancer and population person year (PY) data frames, possibly specific to a race and interval of ages at diagnosis.

Usage

seerSet(canc,popsa,Sex, Race="pooled",ageStart=15,ageEnd=85)seerSet(canc,popsa,Sex, Race="pooled",ageStart=15,ageEnd=85)

Arguments

`canc`	Data frame of cancers that includes agedx, sex, race, yrdx, modx, surv and cancer.
`popsa`	Data frame of population PY at 1-year age resolution.
`Sex`	"male" or "female".
`Race`	"white", "black", "other", or "pooled" (default).
`ageStart`, `ageEnd`	canc and popsa will be reduced to ages in ageEnd>age>=ageStart.

Details

In the output: 1) 0.5 years is added to ages at diagnosis (agedx) to reverse SEER flooring to integers; 2) 0.5 months is added to survival months (again, to reverse flooring) before dividing by 12 to convert to years; 3) year of diagnosis integers are converted to reals by adding to them the month of diagnosis (modx) - 0.5 divided by 12 (note that a modx of 1 represents anytime in the month of January). If ageEnd>85, popsae (extended to ages up to 99) should be used as the input for popsa. If popsa is used, the age86 column of popsa will be replaced by an age column. The age86 and yrbrth columns of a canc are not used and will be removed if they happen to be present; users should manually remove any other columns not needed to minimize seerSet object sizes. Sex and race columns in inputs are removed from outputs as they are specified in other (scalar) seerSet elements. Also removed from canc are cancer factor levels not present for that sex.

Value

A list containing sex specific subsets of canc and popsa and information regarding how they were reduced.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run:  
library(SEERaBomb)
simSeerSet() # without data, a simulated seerSet
#  else, with data ...
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
# trim columns
library(dplyr)  
canc=canc%>%select(-reg,-recno,-agerec,-numprims,-COD,
          -age19,-age86,-radiatn,-ICD9,-db,-histo3) 
popsae=popsae%>%select(-reg,-db)   
seerSet(canc,popsae,Sex="male",ageStart=0,ageEnd=100) 

## End(Not run)
## Not run:  
library(SEERaBomb)
simSeerSet() # without data, a simulated seerSet
#  else, with data ...
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
# trim columns
library(dplyr)  
canc=canc%>%select(-reg,-recno,-agerec,-numprims,-COD,
          -age19,-age86,-radiatn,-ICD9,-db,-histo3) 
popsae=popsae%>%select(-reg,-db)   
seerSet(canc,popsae,Sex="male",ageStart=0,ageEnd=100) 

## End(Not run)

Summarize SEER data.

Description

Shows numbers of cases per cancer in each SEER database and PY in each registry. Sexes are pooled.

Usage

seerStats(canc,popsa)seerStats(canc,popsa)

Arguments

`canc`	Data frame of cancers that includes agedx and cancer columns.
`popsa`	Data frame of population PY at 1-year age resolution.

Value

A list of 2 data.frames with sexes pooled, one of cases with cancer types as rows and as columns, databases, totals, cases >=100 years old or not, and numbers of first-, second-, third- and higher cancers. The second data.frame holds population PY, and PY-weighted ages, per registry.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run:  
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
seerStats(canc,popsae)

## End(Not run)
## Not run:  
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
seerStats(canc,popsae)

## End(Not run)

Chromosome translocation versus age data

Description

This is chromosome translocation versus age data that is pooled across gender and race.

Usage

data(Sigurdson)data(Sigurdson)

Format

A data frame named Sigurdson with the following columns.

age: Age of donor of lymphocytes.
tlcn: Total number of chromosomal translocations per 100 cell equivalents.

Details

The data were obtained using FISH, see reference below. This dataset is loaded automatically with library(SEERaBomb). As such, the function data() is not needed.

References

Sigurdson et al. Mutation Research 652 (2008) 112-121

Examples

library(SEERaBomb)
with(Sigurdson,plot(age,tlcn,cex=2,cex.axis=2,cex.lab=2,las=1,cex.main=2,
               ylab="",main="Translocations per 100 cells"))
library(SEERaBomb)
with(Sigurdson,plot(age,tlcn,cex=2,cex.axis=2,cex.lab=2,las=1,cex.main=2,
               ylab="",main="Translocations per 100 cells"))

Simulate SEER cancers and population person years

Description

Simulates data for two cancers, A and B.

Usage

simSeerSet(N=2e9,yearEnd=2016,ka=1e-5,kb=0.04,Ab=1e-5,
                 tauA=10,tauB=1,delay=1,period=4)simSeerSet(N=2e9,yearEnd=2016,ka=1e-5,kb=0.04,Ab=1e-5,
                 tauA=10,tauB=1,delay=1,period=4)

Arguments

`N`	Number of person years to simulate. Default is roughly that of SEER.
`yearEnd`	Most recent SEER year to simulate.
`ka`	Rate at which cancer A incidence increases linearly with age.
`kb`	Exponential aging rate constant for cancer B incidence.
`Ab`	Exponential amplitude for cancer B incidence.
`tauA`	Survival mean in years for cancer A.
`tauB`	Survival mean in years for cancer B.
`delay`	Years until the beginning of the excess risk of B.
`period`	Duration in Years of the excess risk of B.

Value

A simulated seerSet object with popsa filled using US 2000 Std population proportions and canc with cancers A and B where the incidence of A increases linearly with age and B increase exponentially in age. Survival times are assumed to be exponentially distributed with means of tauA years for A and tauB years for B. Radiation therapy of A is assumed to increase RR of B to 5 uniformly for period years after delay years.

Note

Supported by the Cleveland Clinic Foundation.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
n=simSeerSet()
n=mk2D(n,secondS="B") 
mybrks=c(0,0.75,0.9,1.1,1.25,2,2.5,3,3.5,4,4.75,4.9,5.1,5.25,6)
n=tsd(n,brks=mybrks,trts=c("rad","noRad")) 
D=mkDF(n)%>%filter(cancer1=="A")%>%select(t,RR,L=rrL,U=rrU,trt)
head(D,2)
library(ggplot2)
theme_update(legend.position = c(.8, .815),
             axis.text=element_text(size=rel(1.2)),
             axis.title=element_text(size=rel(1.3)),
             legend.title=element_text(size=rel(1.2)),
             legend.text=element_text(size=rel(1.2)))
g=qplot(x=t,y=RR,col=trt,data=D,geom=c("line","point"),
        xlab="Years Since First Cancer Diagnosis",ylab="Relative Risk")
g+geom_abline(intercept=1, slope=0)+geom_errorbar(aes(ymin=L,ymax=U,width=.05))

## End(Not run)
## Not run: 
library(SEERaBomb)
n=simSeerSet()
n=mk2D(n,secondS="B") 
mybrks=c(0,0.75,0.9,1.1,1.25,2,2.5,3,3.5,4,4.75,4.9,5.1,5.25,6)
n=tsd(n,brks=mybrks,trts=c("rad","noRad")) 
D=mkDF(n)%>%filter(cancer1=="A")%>%select(t,RR,L=rrL,U=rrU,trt)
head(D,2)
library(ggplot2)
theme_update(legend.position = c(.8, .815),
             axis.text=element_text(size=rel(1.2)),
             axis.title=element_text(size=rel(1.3)),
             legend.title=element_text(size=rel(1.2)),
             legend.text=element_text(size=rel(1.2)))
g=qplot(x=t,y=RR,col=trt,data=D,geom=c("line","point"),
        xlab="Years Since First Cancer Diagnosis",ylab="Relative Risk")
g+geom_abline(intercept=1, slope=0)+geom_errorbar(aes(ymin=L,ymax=U,width=.05))

## End(Not run)

Simulate Survival Times

Description

Uses background mortality rates to simulate background survival time for matching sex, age and year.

Usage

 simSurv(d,mrt,rep=1,ltb=NULL,unif=TRUE)simSurv(d,mrt,rep=1,ltb=NULL,unif=TRUE)

Arguments

`d`	Data frame containing sex, agedx, yrdx, surv, and status columns of observed data.
`mrt`	Mortality data binary made by mkMrt().
`rep`	Number of simulated replicates of each observed case.
`ltb`	Life table data binary made by mkLT(). Default of NULL => skip it.
`unif`	TRUE => death time in final year is uniform random draw. False => death at mid-point of year.

Value

Input d with simulation rows added below it, identified as "simulated" in a new column called type.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run: 
library(SEERaBomb)
mkLT()
load("~/data/usMort/ltb.RData")
tail(ltb$Female)

## End(Not run)
## Not run: 
library(SEERaBomb)
mkLT()
load("~/data/usMort/ltb.RData")
tail(ltb$Female)

## End(Not run)

The standard population of the US in 2000

Description

The US population in 2000 for ages up to 100 years. Sexes are pooled.

Usage

stdUS
stdUS

Format

A data frame with the following columns.

age: Sigle-year resolution ages up to 100.
pop: The population within each age group.
prop: Proportion of the total population within each age group.

Details

This population data can be used to map age specific incidence rate vectors into summarizing scalars. It allows cancer incidence rates across different SEER registries to be compared without concerns of differences in age distributions of the populations.

References

http://seer.cancer.gov/stdpopulations/

Examples

library(SEERaBomb)
with(stdUS,plot(age,pop/1e6,type="l",xlab="Age",
     ylab="People (Millions)",main="US Population in 2000"))
library(dplyr)
stdUS%>%filter(age>=85)%>%summarize(weighted.mean(age,w=pop)) 
### so ave age >=85.0 is 89.4
library(SEERaBomb)
with(stdUS,plot(age,pop/1e6,type="l",xlab="Age",
     ylab="People (Millions)",main="US Population in 2000"))
library(dplyr)
stdUS%>%filter(age>=85)%>%summarize(weighted.mean(age,w=pop)) 
### so ave age >=85.0 is 89.4

Summary of seerSet object

Description

Creates a data.frame of cases and median ages and survival times for each cancer and treatment type.

Usage

## S3 method for class 'seerSet'
summary(object, ...)
## S3 method for class 'seerSet'
summary(object, ...)

Arguments

`object`	seerSet object produced by seerSet().
`...`	Included to match arg list of generic summary.

Value

A list that includes: a data.frame of cases, median ages at diagnosis, and survival times, in years, for each cancer and treatment type; a data.frame of person-years by year; and smaller things such as a title, sex, race, and notes. The resulting list is set to class seerSet.summary which has a print method.

Author(s)

Tom Radivoyevitch ([email protected])

Examples

## Not run:  
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
canc=canc%>%select(casenum,race:yrdx,surv,cancer,trt,id) 
popsa=popsae%>%group_by(db,race,sex,age,year)%>%summarize(py=sum(py)) # sum on regs
pm=seerSet(canc,popsa,Sex="male",ageStart=0,ageEnd=100) #pooled (races) male seerSet
pm # no print method for seerSet object, so we see the list
(x=summary(pm)) # print renders summary and plot of PY
class(x)<-NULL #if you want to see the list as is, kill its class.
x    # It then goes through the regular print method for lists.

## End(Not run)
## Not run:  
library(SEERaBomb)
load("~/data/SEER/mrgd/cancDef.RData") #load in canc
load("~/data/SEER/mrgd/popsae.RData") # load in popsae
canc=canc%>%select(casenum,race:yrdx,surv,cancer,trt,id) 
popsa=popsae%>%group_by(db,race,sex,age,year)%>%summarize(py=sum(py)) # sum on regs
pm=seerSet(canc,popsa,Sex="male",ageStart=0,ageEnd=100) #pooled (races) male seerSet
pm # no print method for seerSet object, so we see the list
(x=summary(pm)) # print renders summary and plot of PY
class(x)<-NULL #if you want to see the list as is, kill its class.
x    # It then goes through the regular print method for lists.

## End(Not run)

Package 'SEERaBomb'

Help Index

SEER and A-Bomb Data Analysis Tools

Description

Details

Author(s)

References

See Also

Cancer risk vs years Since Diagnosis of other cancer

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Event vs years Since Diagnosis

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Converts a seerSet$L series to a data.frame

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Get fields from SEER SAS file

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Computes A-bomb incidences

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Computes SEER incidences

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Map ICD9 and ICD-O3 codes to cancers

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Map CODs to strings

Description

Usage

Arguments

Value

Note

Author(s)

See Also

Examples

Map registry codes to acronyms

Description

Usage